Paper: A Simple, Fast, and Effective Reparameterization of IBM Model 2

ACL ID N13-1073
Title A Simple, Fast, and Effective Reparameterization of IBM Model 2
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

We present a simple log-linear reparame- terization of IBM Model 2 that overcomes problems arising from Model 1?s strong assumptions and Model 2?s overparame- terization. Efficient inference, likelihood evaluation, and parameter estimation algo- rithms are provided. Training the model is consistently ten times faster than Model 4. On three large-scale translation tasks, systems built using our alignment model outperform IBM Model 4. An open-source implementation of the align- ment model described in this paper is available from http://github.com/clab/fast align .